To see the problems in building the so-called Internet of Things, look at the trackside switches in what the railroad industry calls "dark territory." These switches are important -- if one is in the wrong position, a train could go off on a sidetrack spur at normal track speed and derail. But these areas are called dark territory because they're lightly used stretches of track in remote areas, where there are no automated signals, and probably no power lines and cellular links. Train operators must visually check that each switch is in the right position.

Union Pacific CIO Lynden Tennison would love to have a monitor that does nothing more than tell dispatchers and engineers which position a switch out in dark territory is in. For such a simple task, "it seems like it ought to be a $100 device, just to me and you living in the consumer tech world," Tennison says. But the sensor would need a power source and a communication link, and it would need to be hardened and weather-resistant. His goal: to get the cost to buy and implement each switch down below $10,000.

Tennison gets a lot of sales calls from analytics software vendors, each promising to help him sort out the data that the Internet of Things can generate for Union Pacific, the largest railroad company in the US. But Tennison's bigger problem is still having to do too much manual data collection. "I keep telling them that if you'll solve my sensor problem and get me a lot of cheap sensors out there that can collect a lot more information for me, I'll buy your analytics engine," he says.

That's the state of the game when it comes to the Internet of Things -- progress, but also frustrating barriers.

Union Pacific says it reduced the number of train derailments caused by failed bearings by 75% by doing near-real-time analysis of data collected by sensors along its tracks, and now it's pouring millions of R&D dollars into new techniques, such as accelerometers on trains that feel for bumps that suggest a bad track. GE Power & Water says it helped Dubai Aluminum improve the fuel efficiency of its gas turbines by 1.5% while increasing output 3.4%, by analyzing sensor-collected operating data. Oil and gas company ConocoPhillips thinks it can save about $250 million a year in drilling costs by doing real-time measurement and analysis along the drill line to fine-tune factors such as speed and pressure on the drill bit. FedEx expects to save $9 million a year using sensors on its trucks that let it schedule dock assignments more efficiently.

Companies are moving more cautiously on the customer-facing Internet of Things, but they're making progress as well. John Deere can do remote, wireless diagnostics of some tractors and combines, for example. Guests at Disney World can wear MagicBands equipped with RFID chips that, when placed next to a reader, connect to their accounts and let them make purchases, access rides, and open their hotel rooms.

But companies are also hitting roadblocks. Union Pacific's Tennison says this whole area of "sensor-based, network-based diagnostic and predictive analytics" will be the biggest technology opportunity in his industry for the next 10 or 15 years. "Having said that, it's not moving as fast as I would like," he says.

Whirlpool CIO Michael Heim says "our toe is in the water on connected devices," as the company figures out the kind of connections customers really want in their homes, and what they'll pay for. Heim does see huge potential, and not just the cliché scenario of your refrigerator knowing all its contents and emailing you when the milk's running low. If customers let Whirlpool track appliance usage remotely, that would be a boon to product development, providing a window into what features people really use. What if the fridge told you when temperatures are varying, suggesting a pending failure, or your icemaker lost water pressure, suggesting a busted pipe might be spraying water all over your kitchen? What if your washer could be diagnosed remotely, since many appliances already generate electronic error codes? Even further out, what if people with elderly parents could monitor appliances remotely -- if Dad opened the fridge four times, used the stove, and ran some laundry, he's probably OK.

While Whirlpool product teams are working on all the foundations for this kind of connectivity, Heim says, "those are more futuristic than you think."

Here are the main IoT challenges companies are wrestling with.

The data isn't good enough. One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.

Let's start with getting enough data. The cost of a sensor includes not just the device, but also the installation, maintenance, connectivity, and power. And even in tightly controlled environments such as a factory, a lot of legacy equipment wasn't built for Internet connectivity, making security and integration problematic.

"We've come a long way, and we're leveraging the heck out of what we do have out there," Tennison says. "I'm just saying to myself, 'If I had 10 times or 20 times as many collection points as I do today, how much better could we get?' That seems to me right now the biggest problem."

Data quality is a problem that GE Power & Water CIO Jim Fowler is putting in front of his $28 billion-a-year unit's CEO and other company leaders. The monitoring and alerting systems GE is developing for maintenance of its gas and wind turbines, for example, draw on many types of data, including customers' operational data and their inventories of replacement parts.

The data collected today is good enough to improve operations -- GE says wind-power company First Wind, for example, improved energy output 3% from existing turbines by monitoring weather and operating conditions and changing the blade pitch on its turbines for better efficiency. But the data

Chris Murphy is editor of InformationWeek and co-chair of the InformationWeek Conference. He has been covering technology leadership and CIO strategy issues for InformationWeek since 1999. Before that, he was editor of the Budapest Business Journal, a business newspaper in ... View Full Bio

We all know the amount of data we collect continues to expand at unprecedented rates, and I agree one of the main barriers to this data is the ability to analyze it efficiently. Data analysis does take expertise, but I believe the onus to employ data scientists should be on the software and analytics vendors — not the end users.

If we make software that provides directional analytics from all of these things, creating an "if this, then that scenario," more people will be able to benefit from that data and act on it sooner: rather than waiting days, weeks or months for direction from an analyst.

And when we're looking at examples of railroad switches, oil wells and even household appliances: time is money.

The beauty of big data is we have access to historical data — from single devices to entire infrastructures — as well as real-time live data. And if information from a sensor had triggered a certain event or scenario in the past, the software measuring that sensor can rely on that historic data to recreate (or prevent) that scenario again.

Interesting article. I don't think that many of us are thinking about the cost of resources for IoT. This includes the cost of bandwidth and power as well as implementation. Right now, based on the quality of data that is coming out of many devices, it's simply not worth all of the initial and operating costs.

We've been hearing about the Internet of Things for years. But it seems as if this is going to take much longer to be a reality than many prognosticators have believed.

It'll be much worse this time. 19th Century telegraphs were able to go back to work after the event was over. 21st century CMOS will be PERMANENTLY RUINED. The machines that will be needed to build replacements will be PERMANENTLY RUINED. Much of the specs, stored in semiconductor or magnetic media will be permanently lost, as will be any electronic equipment built after the late 1960's.

I love how this much technological planning has to go into trains -- which FOLLOW A TRACK -- yet Google and others keep pushing the idea that the consumer market is totally ready for self-driving cars.

An interesting idea that I didn't explore, that I heard in a follow-up chat this week with the folks at M2Mi (thanks Manu), was about who gets access to the Internet of things data. Jim Fowler, quoted in the article, talked about this at the IW Conference -- aobut how GE only gets access to power plant data if the customer continues to see the value in sharing it. But the M2Mi folks brought up other parties who will want access -- for example, the lender who owns a piece of equipment you're leasing, to make sure you're doing the maintenance so it'll have the expected residual value at lease end. A lot to think abot there in terms of who gets access to the data beyond the simple Maker-Owner.

At Packet Dynamics we realized that the 'dark territory' or areas not covered by cellular M2M services exist not only in the U.S but also globally. Infrastructure players whether rail, pipeline, electrical transmission, etc. all need to communicate to end nodes that might never be covered by GSM/LTE. The Cloud can't just exist in the colored areas on AT&T, Verizon, and Sprint's coverage maps.

We provide beyond-line-of-sight communications for M2M and cloud applications using High Frequency radio links. We went back to HF, an old school spectrum that still works, and added the latest in radio technology (waveforms, error correction, etc) to solve real world industrial problems. It's not the glamorous side of the Cloud, but for companies with equipment scattered across the fly-over states or in third world countries it does solve a major problem.

The really large IoT solutions will need the cloud to be truly effective but right now those solutions are few and far between. I can't think of many companies that cover the area that Union Pacific covers with the need to monitor many points along the way. Even companies like UPS or Fedex don't need to see the condition of every mile of road that they travel. I think some government projects could become this big but if you're looking to track trucks for a medium sized company via GPS then moving to the cloud isn't going to be as critical for success.

Insightful article, Chris. Unless you've been out to the remote areas where these pipelines and railines have been built, it's easy to underestimate just how far-flung and distant this infrastructure is -- and what it will take to develop sensors that would stay powered and in touch. But I sense the integration of data question may be equally challenging, and another reason why the IoT will work in some fields, and take much longer in others.